Prototyp · Vorschau: alle Server aus der offiziellen Registry, alle 30 Minuten synchronisiert. Mit Google anmelden, um Bewertungen zu schreiben.
PopNx MCPMCP-Server für alle, die KI nutzen · in einfachen Worten erklärt

open-code-review Skill

von alibaba★ 49.8212 Skills

Installieren

2 Skills (Anweisungssammlungen) von alibaba, die eine KI liest und befolgt · kein MCP-Server

Beschreibung des Anbieters (englisches Original)

Secure, fast, efficient, battle-tested at Alibaba's scale. Hybrid architecture code review tool: deterministic pipelines + LLM Agent, precise line-level comments, built-in multi-language ruleset (NPE, thread-safety, XSS, SQL injection), OpenAI & Anthropic compatible.

Was einen Skill von MCP unterscheidet

MCP verbindet eine KI mit einem externen Dienst oder Daten. Ein Skill ist eine Arbeitsanleitung, die die KI liest und befolgt; er verbindet sich mit nichts.

Funktioniert mit

Claude CodeCodexCursorOpenCode

Laut README des Anbieters und den Ordnern im Repo

Installieren

Ein Skill sind Anweisungen, denen die KI folgt. Lies die Dateien vor der Installation und installiere nur von vertrauenswürdigen Anbietern.

Welche App nutzt du?

  1. Terminal öffnen

    Mac: Command+Leertaste und Terminal tippen · Windows: PowerShell öffnen (der Befehl unten ist für Mac/Linux)

  2. Skill-Ordner nach ~/.claude/skills kopieren

    Lädt das Repo in einen temporären Ordner und kopiert nur Ordner mit einer SKILL.md

    rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/alibaba/open-code-review.git /tmp/popnx-skill && mkdir -p ~/.claude/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.claude/skills && rm -rf /tmp/popnx-skill
  3. Claude Code neu starten, dann /open-code-review tippen oder eine passende Aufgabe stellen; Claude nutzt den Skill

  1. Terminal öffnen

    Mac: Command+Leertaste und Terminal tippen · Windows: PowerShell öffnen (der Befehl unten ist für Mac/Linux)

  2. Skill-Ordner nach ~/.agents/skills kopieren

    Lädt das Repo in einen temporären Ordner und kopiert nur Ordner mit einer SKILL.md

    rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/alibaba/open-code-review.git /tmp/popnx-skill && mkdir -p ~/.agents/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.agents/skills && rm -rf /tmp/popnx-skill
  3. Codex neu starten und eine passende Aufgabe stellen; Codex listet den Skill

  1. Terminal öffnen

    Mac: Command+Leertaste und Terminal tippen · Windows: PowerShell öffnen (der Befehl unten ist für Mac/Linux)

  2. Skill-Ordner nach ~/.cursor/skills kopieren

    Lädt das Repo in einen temporären Ordner und kopiert nur Ordner mit einer SKILL.md

    rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/alibaba/open-code-review.git /tmp/popnx-skill && mkdir -p ~/.cursor/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.cursor/skills && rm -rf /tmp/popnx-skill
  3. Im Agent-Chat von Cursor / tippen und open-code-review wählen

Andere Apps mit Agent Skills (etwa Gemini CLI oder GitHub Copilot) nutzen andere Ordner. Sieh in deren Anleitung nach und kopiere den Ordner mit der SKILL.md dorthin.

Auf GitHub ansehen ↗

Skill-Ordner am 11. Nov. 2026 mit den offiziellen Anleitungen von Claude Code, Codex und Cursor geprüft

SKILL.md vor der Installation lesen

Der folgende Text stammt aus dem Repo des Anbieters, nicht von PopNx MCP

---
name: open-code-review
description: >
  Performs AI-powered code review on Git changes using the `ocr` CLI from
  alibaba/open-code-review. Use when the user asks to review code, review
  a pull request, review staged/unstaged changes, review a commit, or
  compare branches for code quality issues. Produces line-level review
  comments and can automatically apply fixes when requested. With appropriate
  review rules, can detect various types of issues including bugs, security
  vulnerabilities, performance problems, and code quality concerns.
license: Apache-2.0
compatibility: >
  Requires the `ocr` CLI installed (via `npm install -g
  @alibaba-group/open-code-review` or GitHub release binary). Requires a
  configured supported LLM provider before first run (protocols: Anthropic,
  OpenAI Chat Completions, OpenAI Responses, AWS Bedrock).
metadata:
  author: alibaba
  homepage: https://github.com/alibaba/open-code-review
  version: "1.0.0"
---

# Open Code Review

A skill for invoking [open-code-review](https://github.com/alibaba/open-code-review) (`ocr`) — an open-source AI code review CLI that reads Git diffs and generates structured, line-level review comments.

## Workflow

### Step 1: Gather Business Context

Analyze the review target (commits, branch, or changes) to extract concise business context. Pass this context via `--background` to improve review quality.

### Step 2: Run Code Review

**Do not pre-check whether `ocr` is installed** — skip probes like `command -v ocr` or `ocr --version`. Assume the CLI is available and run the review directly; that saves a tool call on the common path. Only if the review fails with `command not found` should you install it per Troubleshooting.

Run the OCR command with appropriate flags. **Always pass business context via `--background`** when available:

```bash
ocr review --audience agent --background "business context here" [user-args]
```

**Argument handling:**

- **Background context** (RECOMMENDED): use `--background "context"` or `-b "context"` to provide business context for better review quality
- **Default** (no user arguments): reviews staged, unstaged, and untracked changes (workspace mode)
- **Specific commit**: use `--commit` or `-c` to review a single commit against its parent
- **Branch comparison**: use `--from <ref>` and `--to <ref>` to review diff between two refs
- **Timeout**: effective timeout per review group = `--timeout` × review rounds. Default `--timeout 15` with default effort `medium` (2 rounds) gives 30 minutes; `low`/`high` give 15/45 minutes.
- **Concurrency**: default concurrency is 8 file workers; reduce with `--concurrency <n>` if rate limits are hit
- **Preview mode**: use `--preview` or `-p` to preview which files will be reviewed without running the LLM
- **Output file**: use `--output <path>` to write the full result to a file instead of stdout. If the command fails with `unknown flag: --output`, do not continue the review with plain stdout. Ask the user whether to upgrade (`npm i -g @alibaba-group/open-code-review@latest`) and wait for the answer before proceeding. After the user confirms and the upgrade succeeds, rerun with `--output`.
- **Installation**: if `ocr` command is not found, install it by running `npm i -g @alibaba-group/open-code-review`

**Common invocation patterns:**

| User says | Command to run |
|-----------|---------------|
| "review my changes" / "review the working copy" | `ocr review --audience agent -b "context"` |
| "review this PR" / "review feature branch" | `ocr review --audience agent -b "context" --from main --to <branch>` |
| "review commit abc123" | `ocr review --audience agent -b "context" --commit abc123` |
| "what would be reviewed?" (dry-run) | `ocr review --preview` |

**Output mode:**

- Always use `--audience agent` to suppress progress UI and emit only the final summary
- **Prevent output truncation**: For large reviews or restricted tool environments, pass `--output /tmp/ocr_out.txt` and inspect the file in full via a file reading tool instead of piping stdout through `tail` or `head`, which drops earlier review comments.

**On failure:** If `ocr review` exits non-zero (e.g. an LLM connection error), do not retry blindly — consult the Troubleshooting section below for the matching fix before re-running.

### Step 3: Report

OCR output includes structured `severity` (critical / high / medium / low) and `category` (bug / security / performance / maintainability / test / style / documentation / other) on each comment. Present results grouped by severity, discarding `low` severity items that are likely false positives or nitpicks.

### Step 4: Fix

Before applying fixes, check whether the user requested automatic fixes:

- If the user explicitly requested "review and fix" or similar, proceed with automatic fixes
- If the user only requested "review" without fix intent, ask for permission before applying any changes

When fixing issues and suggestions:

- Focus on critical, high, and medium severity items
- Apply fixes directly to the code when safe and well-defined
- For complex fixes requiring manual intervention, clearly describe what needs to be done
- Always verify fixes with the user before committing

## Output Format

Each comment in OCR's output contains:

- `path`: File path
- `content`: Review comment text
- `start_line` / `end_line`: Line range (both 0 means positioning failed)
- `category`: Issue category (bug, security, performance, maintainability, test, style, documentation, other)
- `severity`: Issue severity (critical, high, medium, low)
- `suggestion_code`: Optional fix suggestion
- `existing_code`: Optional original code snippet
- `thinking`: Optional LLM reasoning process

Present results grouped by severity using this template:

```markdown
## Code Review Results

**Files reviewed**: N
**Issues found**: X critical, Y high, Z medium

### Critical

- **`path/to/file.java:42`** [bug] — Brief description
  > Recommendation: How to fix

### High

- **`path/to/file.java:26`** [bug] — Brief description
  > Recommendation: How to fix

### Medium

- **`path/to/file.ts:88`** [performance] — Brief description
  > Recommendation: How to fix (if applicable)
```

If no critical, high, or medium severity issues remain after filtering, state: "Review complete — no critical, high, or medium issues found in N files."

**Handling mispositioned comments:**

When `start_line` and `end_line` are both `0`, the comment failed to locate the exact position in the file. In such cases:

1. Read the comment content to understand the issue
2. Examine the target file mentioned in the comment
3. Identify the relevant code section based on the comment's context
4. Apply the fix or suggestion to the correct location

## Custom Review Rules

If the user wants project-specific rules, OCR resolves them in this priority order:

1. `--rule <path>` flag (highest)
2. `<repo>/.opencodereview/rule.json`
3. `~/.opencodereview/rule.json`
4. Built-in system defaults (lowest)

By default, the first matching user rule replaces the built-in system rule. Set `merge_system_rule: true` on a rule entry when the matched system rule and user rule should both be included.

Rule file format:

```json
{
  "rules": [
    {
      "path": "**/*.java",
      "rule": "All new methods must validate required parameters for null",
      "merge_system_rule": true
    },
    {
      "path": "**/*mapper*.xml",
      "rule": "Check SQL for injection risks and missing closing tags"
    }
  ]
}
```

To preview which rule applies to a file before reviewing:

```bash
ocr rules check src/main/java/com/example/Foo.java
```

## Advanced Review Options

Beyond the common flags above, `ocr review` exposes a few groups of controls. Run `ocr review --help` for the complete list.

**Scoping**

- `--exclude '<patterns>'` — comma-separated gitignore-style patterns (for example `--exclude '**/generated/*,**/testdata/*'`), merged with `rule.json` excludes.
- `--background-file <path>` — 

Diskussion

Fragen, Tipps teilen oder melden, dass etwas nicht geht. Sortiert nach „hilfreich“.

Noch keine Beiträge. Stell die erste Frage oder teile einen Tipp.

Beiträge hier stammen von Nutzern, nicht vom PopNx-Team.

Mit Google anmelden, um zu posten

Von GitHub

GitHub-Sterne
49.821
Anbieter
alibaba
Lizenz
Apache-2.0
Letzte Codeänderung
11. Nov. 2026
SKILL.md-Dateien im Repo
4
Themen
agent, agent-skills, code-review, code-review-assistant, harness, repository-level-context